{
 "metadata": {
  "name": "",
  "signature": "sha256:5e33f770c4e884f1f043352c0cb71c62b89b114d5093857100f7d19508da5fe8"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "import networkx as nx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "# wrap a few graph generation functions so they have the same signature\n",
      "\n",
      "def random_lobster(n, m, k, p):\n",
      "    return nx.random_lobster(n, p, p / m)\n",
      "\n",
      "def powerlaw_cluster(n, m, k, p):\n",
      "    return nx.powerlaw_cluster_graph(n, m, p)\n",
      "\n",
      "def erdos_renyi(n, m, k, p):\n",
      "    return nx.erdos_renyi_graph(n, p)\n",
      "\n",
      "def newman_watts_strogatz(n, m, k, p):\n",
      "    return nx.newman_watts_strogatz_graph(n, k, p)\n",
      "\n",
      "def plot_random_graph(n, m, k, p, generator):\n",
      "    g = generator(n, m, k, p)\n",
      "    nx.draw(g)\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print plot_random_graph\n",
      "interact(plot_random_graph, n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n",
      "        generator={'lobster': random_lobster,\n",
      "                   'power law': powerlaw_cluster,\n",
      "                   'Newman-Watts-Strogatz': newman_watts_strogatz,\n",
      "                   u'Erd\u0151s-R\u00e9nyi': erdos_renyi,\n",
      "                   });"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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xySefFLvV37Vr17Dlm2+wZ+9edDM2Rh2FAhKA5CpVcKWwEGPHjsVH//kP2rRp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\nMMBEREQCMMBEREQC/B8GiLKdKDAzgwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb2fc0fef10>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}